Multi-agent reinforcement learning-based exploration of optimal operation strategies of semi-batch reactors
Ádám Sass, Alex Kummer, János Abonyi
Abstract
The operation of semi-batch reactors requires caution because the feeding reagents can accumulate, leading to hazardous situations due to the loss of control ability. This work aims to develop a method that explores the optimal operational strategy of semi-batch reactors. Since reinforcement learning (RL) is an efficient tool to find optimal strategies, we tested the applicability of this concept. We developed a problem-specific RL-based solution for the optimal control of semi-batch reactors in different operation phases. The RL-controller varies the feeding rate in the feeding phase directly, while in the mixing phase, it works as a master in a cascade control structure. The RL-controllers were trained with different neural network architectures to define the most suitable one. The developed RL-based controllers worked very well and were able to keep the temperature at the desired setpoint in the investigated system. The results confirm the benefit of the proposed problem-specific RL-controller.